dashboard

# call relevant packages
library(p8105.datasets)
library(flexdashboard)
library(tidyverse)

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library(plotly)

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##     layout

# load relevant dataset
data("instacart")

Column

Number of Distinct Orders against Hour of Day, by Day of Week

# create a vector of days of the week
myDays <- c(
    "Sunday", "Monday", "Tuesday", "Wednesday"
  , "Thursday", "Friday", "Saturday"
)

# create line chart of order volume over course of day for each day of week
instacart %>%
  # retain variables unique at the order_id level
  select(order_id, order_dow, order_hour_of_day) %>%
  # remove duplicate order IDs
  distinct() %>%
  # group by order_dow and order_hour_of_day
  group_by(order_dow, order_hour_of_day) %>%
  # count order for each hour and for each day of the week
  summarize(count = n()) %>%
  # use myDays vectors to create factor variable
  mutate(order_dow_fct = factor(myDays[order_dow + 1], levels = myDays)) %>%
  # output as plotly line chart
  plot_ly(
      x      = ~order_hour_of_day
    , y      = ~count
    , type   = "scatter"
    , mode   = "lines+markers"
    , color  = ~order_dow_fct
    , colors = "viridis"
  ) %>%
  # add meta-data
  layout(
      title  = ""
    , xaxis  = list(title = "Hour of Day", showticklabels = TRUE)
    , yaxis  = list(title = "Frequency")
    , legend = list(title=list(text =  "<b> Day of Week </b>"))
  )

## `summarise()` has grouped output by 'order_dow'. You can override using the
## `.groups` argument.

Column

Distribution of Hour of Purchase among Top Five Specialty Wines

# rank wines by purchase volume
myProducts <-
  # call data
  instacart %>%
  # filter by specialty wines
  filter(aisle == "specialty wines champagnes") %>%
  # group by product_name
  group_by(product_name) %>%
  # count the wines
  summarize(n_items_ordered = n()) %>%
  # order the wines from most popular to least popular
  arrange(-n_items_ordered) %>%
  # retain top five aisles by purchase volume
  filter(row_number() %in% 1:5) %>%
  # identify top ten wines
  pull(product_name)

# create box plot of hours of day for each wine of interest
instacart %>%
  # filter by wines of interest
  filter(product_name %in% myProducts) %>%
  # create factor variable from product_name
  mutate(product_name = factor(product_name, levels = myProducts)) %>%
  # output as plotly box plot
  plot_ly(
      x      = ~product_name
    , y      = ~order_hour_of_day
    , type   = "box"
    , color  = ~product_name
    , colors = "viridis"
  ) %>%
  # add meta-data
  layout(
      title  = ""
    , xaxis  = list(title = "", showticklabels = FALSE)
    , yaxis  = list(title = "Hour of Day")
    , legend = list(title=list(text =  "<b> Specialty Wine </b>"))
  )

Top Ten Aisles by Purchase Volume

# rank aisles according to number of items ordered from each aisle
instacart %>%
  # group by aisle
  group_by(aisle) %>%
  # count the items in each aisle
  summarize(n_items_ordered = n()) %>%
  # order the aisles from most popular to least popular
  arrange(-n_items_ordered) %>%
  # retain top ten aisles by purchase volume
  filter(row_number() %in% 1:10) %>%
  # adjust case of variables for readability
  mutate(aisle = str_to_title(aisle)) %>%
  # translate ordering to factor variable
  mutate(aisle = reorder(factor(aisle), -n_items_ordered)) %>%
  # output as plotly bar chart
  plot_ly(
      x      = ~aisle
    , y      = ~n_items_ordered
    , type   = "bar"
    , color  = ~aisle
    , colors = "viridis"
  ) %>%
  # add meta-data
  layout(
      title  = ""
    , xaxis  = list(title = "", showticklabels = FALSE)
    , yaxis  = list(title = "Frequency")
    , legend = list(title=list(text =  "<b> Aisle </b>"))
  )